Memory-enhanced spatial-temporal encoding framework for industrial anomaly detection system

The development of modern manufacturing has raised greater demands on the accuracy, response speed, and operating cost of industrial accident warnings. Compared to conventional contact sensors, surveillance cameras can contactlessly capture spatial–temporal information of the open workspace with stable data quality, widely used in industrial process monitoring. However, due to the scarcity of industrial video datasets and the rarity and diversity of abnormal events, existing video-based anomaly detection models perform poorly in manufacturing scenarios. In this regard, we collect two datasets from typical industrial sites and propose a memory-enhanced spatial–temporal encoding (MSTE) framework for automatic industrial anomaly detection. The proposed MSTE framework learns spatial and temporal normality as well as spatial–temporal correlations with parallel structures and simultaneously measures deviations in appearance, motion, and consistency to respond to complex industrial anomalies accurately. Experimental results on public benchmarks and real-world industrial videos show that our method outperforms existing methods and achieves accurate temporal localization of various spatial–temporal anomalies, which helps to improve the safety and reliability of intelligent manufacturing.